User interactions with e-mail, webpages, and other electronic content are tracked and used to facilitate digital marketing functions. Such user interactions are commonly associated with the particular user devices on which they occur. Various techniques have attempted to identify user devices that are related to one another. For example, devices that are expected to be private devices used by a particular user, e.g., a particular individual or household have been identified using probabilistic signals. Identifying such a cluster of devices allows interactions on different devices to be attributed with a particular user, which improves user analytics and advertisement targeting.
One problem, however, is that the techniques used to identify clusters of user devices provide different results over time. For example, differing results occur based on users temporarily or permanently changing residences, buying new devices, losing devices, and selling devices, among numerous other reasons. In one example, based on a first week's data, one cluster has devices 1, 2, and 3 and another cluster has devices 4 and 5 and, based on a second week's data, one cluster has devices 1 and 3, and another cluster has devices 2, 4, and 5. Existing techniques do not adequately determine that clusters of devices identified at different time periods are for the same user. For example, existing techniques do not adequately determine whether the cluster from the first week with devices 1, 2, and 3 and the cluster from the second week with devices 1 and 3 are the same cluster, i.e., whether the devices should be associated with a single user or different users.